Over-the-air (ota) mobility services platform

ABSTRACT

An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/687,723, filed Jun. 20, 2018, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates generally to a cloud-based, OTA softwareplatform for providing mobility services to mobility clients.

BACKGROUND

Although a majority of new vehicles are expected to have embeddedconnectivity within a few years, automotive Original EquipmentManufacturers (OEMs) have not fully taken advantage of the embeddedconnectivity to remotely managed vehicles to reduce warranty costs andincrease customer satisfaction. Furthermore, vehicle features andfunctional complexity continue to increase, resulting in more ElectronicControl Units (ECUs) with more software that require maintenance andupgrades through vehicle life cycles. OEMs need a robust, scalable andefficient vehicle software update management for their customers. Withthe advent of self-driving vehicles, software update management willbecome even more challenging because self-driving vehicles have moresoftware to update than manned vehicles.

SUMMARY

An over-the-air (OTA) mobility service platform (MSP) is disclosed thatprovides a variety of OTA services, including but not limited to:updating software OTA (SOTA), updating firmware OTA (FOTA), clientconnectivity, remote control and operation monitoring. In some exemplaryembodiments, the MSP is a distributed computing platform that deliversand/or updates one or more of configuration data, rules, scripts andother services to mobility clients. In some exemplary embodiments, theMSP optionally provides data ingestion, storage and management, dataanalytics, real-time data processing, remote control of data retrieving,insurance fraud verification, predictive maintenance and social mediasupport.

In an embodiment, an OTA mobility services platform receives a pluralityof user inputs specifying a mobility client model describing and aplurality of software models that are associated with the mobilityclient model. The mobility client model defines a plurality of hardwarecomponents installed on a mobility client that execute software definedby the software models. The plurality of software models also defineinterdependencies between one or more versions of the plurality ofsoftware models. Update files are generated for software updates storedin a data repository. Download information files are also generated thatinclude instructions for downloading and installing the update files.The instructions define an installation order hierarchy that preservesthe interdependencies between the one or more versions of the pluralityof software models. A distribution task is generated that includes astart time, an end time and a mobility client group, and installationtasks are generated for mobility clients in the mobility client group.In accordance with the installation tasks, the download informationfiles are transferred to the mobility clients in the mobility clientgroup. In accordance with the download information files, the updatefiles are transferred to the mobility clients in the mobility clientgroup.

In an embodiment, the installation tasks are monitored to detect anissue with the installation tasks, and in accordance with detecting anissue and responsive to user inputs, the issue is assigned to a memberof a user class of the mobility services platform based on an issuetype. A status of the installation tasks is changed after the issue isresolved.

In an embodiment, the mobility client group is determined based onlocation data provided by the mobility clients in the mobility clientgroup.

In an embodiment, the mobility client group is determined based on thelocation data and a geofence.

In an embodiment, the OTA mobility services platform is a distributedcomputing platform that includes a data stream processing pipelinearchitecture for processing data feeds from mobility clients using ascalable publication/subscription message queue as a distributedtransaction log.

In an embodiment, the OTA mobility services platform comprises multipleinstances of software that run concurrently and communicate with eachother over a message bus.

In an embodiment, the OTA mobility services platform creates virtualrepresentations of the mobility clients that include unique identifiersfor the mobility clients and data structures with one or more fieldsthat provide an interface operable for sending and receiving data orservices from one or more data or service providers that are external tothe OTA mobility services platform. The virtual representation is storedin one or more databases accessible by the OTA mobility servicesplatform.

In an embodiment, a mobility client of an OTA mobility services platformreceives a download information file including instructions fordownloading and installing update files from a data repository. Theinstructions define an installation order hierarchy that preservesinterdependencies between one or more versions of a plurality ofsoftware models defining software installed on the mobility client. Inaccordance with the instructions, the update files are downloaded to themobility client. In accordance with the installation order hierarchy,installation of the update files is initiated on the mobility client.

In an embodiment, the installation order hierarchy is determined by atopological sort.

In an embodiment, the download information file includes links to theupdate files stored in a network-based data repository.

In an embodiment, an OTA mobility services platform for updatingsoftware or data on a mobility client causes a graphical user interface(GUI) to be displayed that includes a workflow canvass. One or more userinputs are received that cause a distributed workflow to be created ordisplayed on the workflow canvass. The distributed workflow includes aplurality of connected display objects, each display object representinga node in the distributed workflow. Configuration data for a data sourceobject is received that selects one of a data streaming pipeline or adata repository, the data streaming pipeline streaming mobility clientdata received OTA from a mobility client or the data repository storinghistorical mobility client data. In accordance with the configurationdata selecting the data streaming pipeline, applying a data processor tothe mobility client data in the data streaming pipeline, the dataprocessor represented by one of the display objects in the distributedworkflow. In accordance with the configuration data selecting the datarepository, applying the data processor to the historical mobilityclient data stored in the data repository.

In an embodiment, at least one of the display objects represents a rangefilter that is applied to data to limit the data to a range ofcontiguous values.

In an embodiment, at least one of the display objects represents a datefilter that excludes data from further processing based on timestampsfalling inside or outside a specified date or time range.

In an embodiment, at least one of the display objects represents afilter expression combining one or more values, operators or databasefunctions.

In an embodiment, the configuration data selects the data repository,and at least one of the display objects represent a data transformationthat joins two or more columns of data values in a database table storedin the data repository.

In an embodiment, the configuration data selects the data repository,and at least one of the display objects represent a data transformationthat sums data values in a single column of a database table stored inthe data repository.

In an embodiment, applying the data processor further includesextracting features from data in the data repository, splitting thefeatures into a training data set and test data set; training a modelusing the training data set, testing the model using the test data set,and storing the model in the data repository.

In an embodiment, applying the data processor further includes applyingthe model to the mobility client data in the data streaming pipeline,and using an output of the model to predict a state of the mobilityclient.

In an embodiment, the mobility client is a self-driving vehicle and themobility client data is sensor data provided by one or more sensorsembedded in the self-driving vehicle.

In an embodiment, the one or more user inputs include one or more dragand drop operations, and one or more display objects representing nodesin the distributed workflow are dragged from a tool set in the GUI anddropped on the workflow canvass.

In an embodiment, a method includes: receiving, by one or moreprocessors of an OTA mobility services platform, sensor data from amobility client; storing, by the one or more processors, the sensor datain a data structure, the data structure associated with a uniqueidentifier for the mobility client and being a copy of a data structurestored on the mobility client; associating, by the one or moreprocessors, the data structure with an instance of an data analysisprocess running on the mobility services platform, the analyticalprocess applying an analytical model to the sensor data; processing, bythe one or more processors, the sensor data in a data stream processingpipeline architecture, the processing including applying the dataanalysis process to the sensor data and predicting one or moremaintenance events for the mobility client based on output of the dataanalysis process; identifying, by the one or more processors, a severitystate for each of the one or more maintenance events; and performing, bythe one or more processors, one or more actions in accordance with theone or more maintenance events and severity states.

In an embodiment, the one or more actions include sending an alertmessage to the mobility client.

In an embodiment, the one or more actions include generating a reportincluding a description of the maintenance action and at least one of aplot of the sensor data or an occurrence history.

In an embodiment, the data analysis process includes applying a machinelearning algorithm to the sensor data that is based on at least one of adecision tree regression, random forest regression or gradient-boostedtree regression.

In an embodiment, the one or more maintenances events includes apredicted life expectancy of one or more parts of the mobility client.

In an embodiment, the one or more maintenance events includes at leastone of replacing a worn part of the mobility client or replacing a fluidused by the mobility client.

In an embodiment, the sensor data includes data output by a brake padsensor and a brake fluid sensor, and the one or more maintenance eventsinclude a predicted life expectancy of the brake pads and a predictedbrake fluid change time.

In an embodiment, the predicted life expectancy of the brake pads has aseverity state based on a mileage range calculated from the sensor data.

In an embodiment, the predicted braked fluid change time has a severitystate based on brake fluid viscosity index calculated from the sensordata.

In an embodiment, performing one or more actions includes downloadingdata to configure or reconfigure one or more software components on themobility client

In an embodiment, a method comprises: obtaining, by one or moreprocessors of an OTA) mobility services platform, insurance claim data;determining, by the one or processors, one or more fraud indicatorsbased on the insurance claim data; obtaining, by the one or moreprocessors, vehicle data based on the one or more fraud indicators;comparing, by the one or more processors, the vehicle data withindicator ranges; and responsive to the comparing, generating, by theone or more processors, a fraud verification report.

In an embodiment, the vehicle data is included in an on-boarddiagnostics report received through an OTA service from a vehicle.

In an embodiment, the vehicle data is historical vehicle data obtainedfrom a data repository of the mobility services platform.

In an embodiment, user input is received that indicates approval orrejection of the insurance claim based on the fraud verification report.

In an embodiment, the one or more fraud indicators includes at least oneof brake pedal force, de-acceleration peak or brake imbalance.

In an embodiment, the vehicle data includes at least one of speed,location or sensor data.

In an embodiment, the vehicle data is compared to minimum and maximumvalues for the fraud indicators.

In an embodiment, the fraud verification report includes a claimshistory associated with a claimant.

In an embodiment, the fraud verification report is sent by the OTAmobility services platform to an insurance provider that provided theinsurance claim data.

In an embodiment, the OTA mobility services platform notifies a claimsmanager by at least one of e-mail notification, alert notificationthrough a dashboard provided by the OTA mobility services platformthrough a web service or text message

In an embodiment, a method includes: selecting, by one or moreprocessors of an OTA mobility services platform, one or more keywordsfrom one or more mobility client models; importing, by the one or moreprocessors, social media data retrieved from one or more social mediaplatforms; extracting, by the one or more processors, portions of thesocial media data that match the one or more keywords; identifying, bythe one or more processors, one or more owners of mobility clientsdescribed or otherwise identified by the one or more key words; andsending, by the one or more processors, one or more communications tothe one or more owners that include some or all of the matching socialmedia data.

In an embodiment, the social media data is filtered to remove extraneoussymbols and words.

In an embodiment, the one or more communications include electronic mailcommunications that include a message for the owner.

In an embodiment, the message includes at least a portion of the socialmedia data.

In an embodiment, the social media data is a Tweet®.

In an embodiment, the one or more keywords include an IoT device name.

In an embodiment, the one or more keywords include a make or model of avehicle.

One or more embodiments of the disclosed mobility service platformprovide one or more of the following advantages. The MSP provides userswith a reliable, efficient, secure and scalable cloud based managementsolution to remotely manage and update software and other data onmobility clients. The MSP provides a number of OTA services includemonitoring, diagnostics and prognostics to optimize performance andutilization of mobility clients. Sensor data from mobility clients arecombined with historical data, human expertise and fleet and simulationlearning to improve the outcome of prognostics for mobility clients, todiscover the root cause of issues related to OTA operations and toresolve those issues.

The details of the disclosed implementations are set forth in theaccompanying drawings and the description below. Other features, objectsand advantages are apparent from the description, drawings and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system that includes a mobility servicesplatform for providing mobility services to mobility clients, accordingto an embodiment.

FIG. 2 is a block diagram of a mobility services platform, according toan embodiment.

FIG. 3A is a conceptual diagram of OTA client software architecture,according to an embodiment.

FIG. 3B is an event diagram illustrating OTA client processing ofsoftware update packages, according to an embodiment.

FIG. 4A is a flow diagram of a planning process for a software update,according to an embodiment.

FIG. 4B is a diagram illustrating software dependencies between softwaremodels, according to an embodiment.

FIG. 5 illustrates a visualization including a map that allows a DM todraw a geofence for software update distributions, according to anembodiment.

FIG. 6 is a flow diagram for an issue detection and resolution process,according to an embodiment.

FIG. 7 illustrates a data analyst dashboard, according to an embodiment.

FIG. 8A illustrates a properties pane of a data source object, accordingto an embodiment.

FIG. 8B illustrates a mapping pane of the data source object, accordingto an embodiment.

FIG. 8C illustrates a properties pane of a filter object, according toan embodiment.

FIG. 8D illustrates a mapping pane of the filter object, according to anembodiment.

FIG. 8E illustrates a properties pane of a join object, according to anembodiment.

FIG. 8F illustrates a mapping pane of the join object, according to anembodiment.

FIG. 9A is a flow diagram of a data mining process for generating amodel using a machine learning algorithm, according to an embodiment.

FIG. 9B is a flow diagram of a process for training and evaluating amodel, according to an embodiment.

FIG. 10A is a flow diagram of a predictive maintenance process for avehicle brake system, according to an embodiment.

FIG. 10B is a more detailed flow diagram of a predictive maintenanceprocess for a vehicle brake system, according to an embodiment.

FIG. 10C is an example predictive maintenance report, according to anembodiment.

FIG. 11A is a block diagram of a claims management system, according toan embodiment.

FIG. 11B is an example fraud claims verification report, according to anembodiment.

FIG. 12 is a flow diagram of a process for using social data for vehiclemanagement, according to an environment.

FIG. 13 illustrates a security process for software update packagedistribution, according to an embodiment.

FIG. 14 is an example home screen including a variety of visualizations,according to an environment.

FIG. 15 is a system block diagram of a self-driving vehicle, accordingto an embodiment.

FIG. 16 is a system block diagram of a distributed computingarchitecture for a vehicle, according to an embodiment.

The same reference symbol used in various drawings indicates likeelements.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first processorcould be termed a second processor, and, similarly, a second processorcould be termed a first processor, without departing from the scope ofthe various described embodiments. The first processor and the secondprocessor are both processors, but they are not the same processor.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms ““an” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Itwill also be understood that the term and/or” as used herein refers toand encompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

System Overview

FIG. 1 is a block diagram of a system that includes a mobility servicesplatform for providing mobility services to mobility clients, accordingto an embodiment. System 100 includes Mobility Services Platform (MSP)101, mobility clients 102 and system clients 103. Each of these entitiescommunicates with the other entities through network 104. In anembodiment, MSP 101 is a secure, distributed computing platform thatprovides a variety of OTA services, including but not limited to:updating software OTA (SOTA), updating firmware OTA (FOTA), clientconnectivity, remote control and operation monitoring. In addition tosoftware and firmware updates, MSP 101 may deliver and/or update one ormore of configuration data (e.g., vehicle or component models), rules(e.g., data stream processing patterns), scripts (e.g., securityscripts) and services (e.g., a software plugin to the mobility client).MSP 101 also provides data ingestion, storage and management, dataanalytics, real-time data processing and remote control of dataretrieving.

Mobility clients 102 include any type of vehicle, vessel or device,including any Internet of Things (IoT) device. Some examples of mobilityclients include but are not limited to: any type of vehicle or vessel(including electric, hybrid and self-driving vehicles or vessels),automated delivery robots, drones, etc. Some examples of vehiclesinclude any 4-wheel, three-wheel or two-wheel vehicles (e.g., passengercars, motorcycles, trucks, buses, off-road vehicles, Trikes,Reverse-Trikes, ATVs, Tuk-Tuks, mobility scooters). System clients 103can include any individual or entity that provides software, data orcontent to mobility clients 102. System clients 103 can communicate withMSP 101 using a web portal, data feed, mobile application or any othersuitable communication mode. Network 104 is any wireless communicationsnetwork, or collection of networks, suitable for data or SOTAbi-directional communications with a large number of mobility clients.

An IoT device is any physical device, vehicle, appliance or other objectthat includes one or more of electronics, software, sensors, actuatorsand connectivity, and which enables the IoT devices to connect andexchange data (e.g., stereo/mono camera, LIDAR, radar, navigationsystem, entertainment system, ECU, TCU, etc.).

Example Mobility Services Platform (MSP)

FIG. 2 is a block diagram of MSP 101, according to an embodiment. MSP101 includes a plurality of interconnected processors, routers, hubs,gateways, storage devices and other hardware that communicate data,commands and signals, over one or more buses. In an embodiment, MSP 101is a distributed computing platform that includes a data streamprocessing pipeline architecture for processing real-time data feedsusing a scalable publication/subscription message queue as a distributedtransaction log.

In some exemplary embodiments, MSP 101 includes message bus 201, messageengine 202 and database(s) 207. In some exemplary embodiments, MSP 101optionally includes transformation engine 203. In some exemplaryembodiments, MSP 101 optionally includes operation engine 204. In someexemplary embodiments, MSP 101 optionally includes intelligence engine205. In some exemplary embodiments, MSP 101 optionally includes analyticengine 206. In an embodiment, one or more of engines 202-206 are each aninstance of a software method that runs on one or more servers of MSP101. These software instances are configured to communicate with eachother using message bus 201. Multiple instances of engines 202-206 canrun concurrently. Engines 202-206 provide OTA services (e.g., softwareupdates, client connectivity, remote control and operation monitoring),and data services (e.g., data ingestion, data storage/management, dataanalytics, real-time processing and data retrieving control).

In an embodiment, a load balancer (not shown) running on one or moreservers manages connection requests from mobility clients 102 bylistening on one or more ports connected to mobility clients 102 toaccess OTA services. The load balancer forwards requests to a backendserver that has at least one instance of message engine 202 running. Inan embodiment, the load balancer maintains persistence (server affinity)to ensure that connections and subsequent requests from mobility clients102 are sent to the same server after a service interruption (e.g., dueto a lost connection). Messages sent by mobility clients 102 can beformatted in any known data-interchange format, such as ExtensibleMarkup Language (XML) or Java® Script Object Notation (JSON).

In an embodiment, message bus 201 is implemented by a distributedstreaming platform. The distributed streaming platform publishes andsubscribes to streams of messages (also referred to as “records”),stores the streams of messages in database(s) 207 and provides areal-time streaming data pipeline that can transfer the streams ofmessages between engines 202-206. An example message bus 201 is theApache Kafka® distributed streaming platform. In an embodiment,consumers of messages can subscribe to a particular “topic” to retrievemessages from message bus 201 for that topic. A topic is a category orfeed name to which messages are published. The topics aremulti-subscriber and can have zero, one, or many consumers thatsubscribe to the data written to the topic. Raw input data is consumedfrom topics and then aggregated, enriched, or otherwise transformed intonew topics for further consumption or follow-up processing by otherconsumers of MSP 101.

In an embodiment, data structure 208 (hereinafter, also referred to as a“digital twin”) is created for mobility client devices 102 and stored inone or more databases 207. A digital twin is a cloud-based virtualrepresentation of a mobility client. A digital twin can be used formonitoring, diagnostics and prognostics to optimize performance andutilization of mobility clients 102. Sensor data can be combined withhistorical data, human expertise and fleet and simulation learning toimprove the outcome of prognostics. MSP 101 uses digital twins ofmobility clients 102 to discover the root cause of issues related to OTAoperations and to resolve those issues. Each instance of each engine202-206 can create a copy of data structure 208 for a particularmobility client, and read or write data to any field 208 b-208 f in datastructure 208. A copy of data structure 208 for every mobility client102 can be stored on a plurality of distributed databases. A backgroundprocess implemented by MSP 101 can maintain coherency between differentcopies of data structure 208 stored on distributed databases.

In an embodiment, data structure 208 includes a number of fields forexchanging data between mobility clients 102 and MSP 101. In the exampleshown, the fields include but are not limited to: Mobility Client IDfield 208 a, State Flow/Status field 208 b, Update Info field 208 c,Control Info field 208 d, Data Info field 208 e and Extended Datafield(s) 208 f. These fields are exemplary and other embodiments of datastructure 208 can have more or fewer fields.

Mobility Client ID 208 a can be a Universally Unique Identifier (UUID)that uniquely identifies a mobility client. In an embodiment, MobilityClient ID 208 a stores a Vehicle Identification Number (VIN) that can beused to uniquely identify a mobility client. State Flow/Status field 208b includes state and session-specific information for persistence(server affinity). Update Info field 208 c includes informationassociated with a particular software update, such as a downloadinformation file provided by intelligence engine 205. Control Info field208 d includes commands for remote control of an OTA client, such as adisable command to disable a particular software version installed onthe mobility client. For inbound messages, Data Info field 208 eincludes the name of the mobility client, a timestamp and a link to asoftware package for the mobility client that is stored in in a softwarepackage repository. For outbound messages, the Data Info field 208 e isused to send data and commands to the mobility client.

Extended Data field(s) 208 f are used to send and receive data orservices. Extending Data fields 208 f can include links to data orservice providers (e.g., URIs, URLs, pointer) or an applicationprogramming interface (API). For example, if a mobility client requestsa service or data that is hosted by a third party data or serviceprovider external to the MSP, then Extended Data field(s) 208 f canprovide an interface to the data or service and the MSP will handle thenecessary connections to the third party server computers to request andreceive results generated by the third party server computers. In thismanner, each mobility client will have a digital twin with a number ofcustomized Extended data filed(s) 208 f based on the particular servicesor applications subscribed to by the mobility client. For example, ifthe mobility client wants to subscribe to a traffic or weather service,access to the traffic or weather service is provided through theExtended Data field(s) 208 f of the digital twin for that mobilityclient. The services can be hosted on third party server computers(e.g., hosted by server farm) or by MSP server computers. The servicesthat can be subscribed to include any service that is available throughany mobile application accessible by, for example, a smartphone. Thisfeature is advantageous because it allows integration of mobileapplications that the user is already subscribed to on their smartphoneor tablet computer to be available in their vehicle through, forexample, an entertainment system or vehicle computer.

In an embodiment, third party software can be hosted on servers of MSP101 and Extended Data field(s) 208 f provide access to the servicesthrough an API or other interface mechanism. In an embodiment, userprofile data can be sent to MSP 101 in Extended Data field(s) 208 f.Personal profile information can be any data related to the personalpreferences of an operator of the mobility client, including but notlimited to: climate control data, seat and mirror adjustment data,entertainment preference data (e.g., radio presets, music playlists),telephone contact lists, navigation data (e.g., history data,destination locations) and any other data that is personalized to aparticular operator of the mobility client.

In an embodiment, Extended Data field(s) 208 f can include multiplepersonal profiles. For example, each member of a family who shares themobility client can have their own personal profile. Also, if themobility client is part of a fleet of mobility clients (e.g., taxis,rental cars, company vehicles), then personal profiles for each operatorcan be stored in database(s) 207.

In another embodiment, extended data fields(s) 208 f can be used for aMobile Device Management (MDM) service. For example, MDM data downloadedonto the mobility client can allow or restrict employees from usingcertain features, including imposing and enforcing policies on themobility client, such as policies to optimize mobility client usage andsecure data. For example, MDM data can configure a mobility client toreport mobility client data to MSP 101 where it can be further analyzed.Mobility client data can include but is not limited to: location data(e.g., timestamp, latitude, longitude, and altitude), sensor data (e.g.,acceleration data, gyroscope data) and environment data (e.g.,temperature data). In an embodiment, the location data can be used todetermine if a mobility client has entered or exited a geofence (avirtual geographic boundary) to trigger a download of a software packageor perform some other location-based service, as described in referenceto FIG. 5.

In an embodiment, geofence crossings can be used to determine if acorporate policy has been violated. For example, drivers for a taxiservice may be prohibited from traveling within or outside a certaingeographic region enclosed by a geofence. If the mobility client is aself-driving vehicle, then Extended Data fields(s) 208 f can includemobility client data specific to self-driving vehicles, such as LiDAR,ultrasonic sensors, radar, Global Navigation Satellite System (GNSS),stereo camera and map data. In an embodiment, the mobile client data canbe used by analytic engine 206 to detect and predict various maintenanceproblems with mobility clients 102, as described in reference to FIGS. 9and 10.

After the load balancer (not shown) receives a message from a mobilityclient, the load balancer sends the message to a MSP server that isrunning an instance of message engine 202. Message engine 202 providesan end-point to communicate with one or more mobility clients 102 andsupports both inbound and outbound message processing. The number ofmessage engines 201 that are concurrently running is based on the numberof active connections with mobility clients 102. In an embodiment, theload balancer and/or message engine 202 implements one or more protocolsfor communicating with mobility clients 102, including but not limitedto: Transmission Control Protocol/Internet Protocol (TCP/IP), HypertextTransfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS),Message Queue Telemetry Transport (MQTT) protocol and Open MobileAlliance Device Management (OMA-DM) protocol.

In an embodiment, message engine 202 reads the message header andperforms version authentication. An integration Application ProgrammingInterface (API) allows third party applications to communicate withmessage engine 202 over a network (e.g., the Internet). For example, theSOTA service may be unavailable, or the software update may be too largeto transfer using the SOTA service. In such cases, the integration APImay be used by an application running on a personal computer or mobiledevice to upload or deliver a software package to a personal computerover a network. After the package is downloaded to the personal computerit can be transferred to a Universal Serial Bus (USB) thumb drive. Forexample, a technician in a repair shop or dealership can download asoftware package from MSP 101 to a personal computer, transfer thepackage to a thumb drive, and then connect the thumb drive directly to aport of a vehicle computer to transfer the software package to thevehicle.

Transformation engine 202 reads the message body and transforms themessage body into a common message data format used by message bus 201(e.g., the Kafka® streaming format).

Operation engine 204 supports data operations, software operations andsystem issue management. Operation engine 204 provides a Web portal andmobile user interface (UIs) to communicate with system clients 103(e.g., OEMs, software developers). Operation engine 204 generatesreports with visualizations (e.g., charts, tables), which can be viewedon the Web portal and/or mobile UIs, and sends notifications/alerts tosystem clients 103 using various modes of communication (e.g., email,push notification, text message). Operation engine 204 also provides aservice API that allows system clients 103 to access mobility servicesusing their proprietary applications. In an embodiment, the service APIsupports a payment system that manages billing-based software updatesusing data retrieved through the service API.

Intelligence engine 205 supports various OTA operations, includingsoftware packaging, software dependency checking, scheduling andmonitoring.

Analytic engine 206 supports business intelligence, including reportgeneration and alert detection. Analytic engine 206 also provides anInteractive Development Environment (IDE) that includes a dashboard andworkflow canvass that allows a data analyst to build, test and deploydistributed workflows using real-time message streams or a database as adata source, as described more fully in reference to FIGS. 7 and 8.

In an embodiment, database(s) 207 include(s) a relational database(e.g., SQL database) and a distributed NoSQL database (e.g., ApacheCassandra™ DBMS with Elasticsearch™ service) for storing messages, datalogs, software packages, operation history and other data. The datastored can be either structured or unstructured. In an embodiment,engines 202-206 can communicate with database(s) 207 over data accesslayer (DAL) 209 using, for example, the Java® EE data access object(DAO).

Example OTA Client Software Architecture

FIG. 3A is a conceptual diagram of OTA client software architecture,according to an embodiment. As used in this specification, the term“updates” includes any type of digital computer instructions or data,including but not limited to: software, firmware, object code, machinecode, scripts, Java® bytecode, data and metadata. The examples thatfollow refer to updating firmware of an Electronic Control Unit (ECU) orTransmission Control Unit (TCU). ECUs and TCUs are associated withautomobiles (See FIG. 16). The description that follows, however, isalso applicable to any electronic controller, microcontroller unit (MCU)or other hardware processor that executes an instruction whether or notembedded in any electronic device or IoT device, Application SpecificIntegrated Circuits (ASICs), System-on-Chip (SoC) and Central Processingunits (CPUs).

In an embodiment, OTA client software architecture 300 includes OTAclient module 301, delta manager 302 and installation manager 303. Inexemplary embodiments, architecture 300 optionally includescommunication manager 304. In exemplary embodiments, architecture 300optionally includes ECU manager 305. In exemplary embodiments, OTAclient module 301 optionally includes one or more of download manager306, data abstraction layer (DAL) 307, local database 308 and utilitymodules 309. In exemplary embodiments, delta manager 302 optionallyincludes delta compression tools 310 (e.g., XDELTA3, BSDIFF). Inexemplary embodiments, installation manager 303 optionally includes oneor more of USB module 311 and Updater module 312. In exemplaryembodiments, communication manager 304 optionally includes businterfaces 313. In exemplary embodiments, ECU manager 305 optionallyincludes ECU updater module 314.

Download manager 306 of OTA client module 301 communicates with MSP 101over network 104 using a wireless protocol (e.g., OMA-DM 2.0 protocol).DAL 307 provides an interface to connect to local database 308 (e.g.,SQLite database). DAL 307 is an object-oriented system that provides aninterface to connect database 308 with external applications andperforms database-related operations. DAL 307 also stores metadatarelated to installation logs generated by OTA client module 301 and ECUmanager 305 during installation operations on ECUs. Utility modules 309include a logger module for generating communication logs, HTTP modulefor managing HTTP connections, OpenSSL module for managing OpenSSLconnections and JSON module for managing JSON connections.

Delta manager 302 is responsible for generating the difference betweentwo files using one of delta compression tools 310. Delta manager 302 isalso capable of reconstructing a new updated software image by applyingdelta (diff) files on base files of the software being updated.

Update module 312 of installation manager 303 provides update androllback operations. The rollback operation provides a fail-safemechanism whereby if during an update process, the installation manager303 fails to update the base firmware with new firmware, the basefirmware is restored by the rollback operation to the last firmwareversion. USB module 311 manages connections with USB thumb drives toreceive updates as an alternative to the OTA service.

Communication manager 304 manager includes bus interfaces 313, such asController Area Network (CAN), FlexRay™, Ethernet and DBUS. CAN is avehicle bus standard designed to allow microcontrollers and devices tocommunicate with each other in applications without a host computer.DBUS is an inter-process communication (IPC) and remote procedure call(RPC) mechanism that allows communication between multiple computerprograms concurrently running on the same machine.

Communication manager 304 provides data transfer operations betweenapplications. In an embodiment, communication manager 304 is a dynamiclibrary where applications can link to communication manager 304 to useits services. A request from an application linked with thecommunication manager 304 to send data is broadcast over a bus (e.g.,CAN, DBUS). Similarly, a request from any application linked withcommunication manager 304 to receive data would read data over the bus.The selection of channel is determined through configuration file 315,which has a one-to-one mapping between applications and correspondingservices of the communication manager 304. An interface abstractionlayer is used support interfaces other than CAN, such as Ethernet andFlexRay™.

ECU manager 305 is the core engine which coordinates communicationbetween OTA client module 301 and a particular target ECU or dataclient, as well as manage its own internal state. ECU updater module 314is responsible for transferring new ECU software to target ECUs, whichit receives from OTA client module 301. ECU updater module 314 is alsoresponsible for transferring target ECU data (e.g., sensor data) to dataclients. In an embodiment, ECU updater module 314 can be ported into theECUs to perform Software Components Management Object (SCOMO)operations, which is part to of the OMA-DM standard. ECU manager 305communicates with target ECUs to transfer files using Unified DiagnosticServices (UDS) protocol on CAN, FlexRay™ or Ethernet as the PhysicalLayer. ECU manager 305 also responds to requests from OTA client module301 for software version information.

Example OTA Client Processing

FIG. 3B is an event diagram illustrating mobility client processing ofupdate packages for a single ECU upgrade, according to an embodiment.MSP 101 will send a download information file for updating software (inthis example firmware) for a single ECU upgrade without aninterdependency. In a first download phase, OTC client module 301retrieves the download information file (e.g., a JSON file) from MSP101, and, in a second download phase, downloads the firmware from a datarepository using links in the download information file. The downloadinformation file is stored by OTC client module 301 in local database308 (e.g., in local disk memory). The download information file includesinstallation commands (e.g., scripts) and links to software files storedin a data repository.

OTA client module 301 invokes an install command via communicationmanager 304. Relevant information (e.g., ECU Identifier (ID), firmwarepath, firmware version) for the install command is sent to communicationmanager 304. Communication manager 304 informs ECU manager 305 totrigger an update firmware command. ECU manager triggers the updatefirmware command, and reports back the status of the update operation(e.g., a success or error code) to OTA client module 301 viacommunication manager 304. OTA client module 301 then reports theappropriate success/fail alerts to MSP 101. After the report is sent,the next update for another ECU ID is triggered. This sequence ofoperations is repeated for all the received commands.

In an embodiment, the download information file retrieved from MSP 101is encrypted for security using symmetric key encryption or any othersuitable encryption technology (e.g., elliptic curve encryption). Priorto installing any updates, OTA client module 301 and MSP 101 perform anauthentication procedure to ensure the download information file wasretrieved from MSP 101 (a trusted source). For example, the downloadinformation file can be digitally signed, and the authentication of theMSP server, client and download information file can be performed usingpublic-key cryptology methods, as described more fully in reference toFIG. 13.

After successful authentication, the contents of the downloadinformation file are decrypted, and OTA client module 301 downloads thefirmware files from the data repository. For example, the downloadinformation file can include a Uniform Resource Identifier (URI) foreach firmware file, such as a Uniform Resource Locator (URL). The URLprovides a path for OTA client module 301 to download the firmware filefrom the data repository. In an embodiment, the data repository is adatabase hosted by a third-party hosting service that is accessiblethrough a Web service (e.g., an Amazon S3® database).

In another embodiment, MSP 101 will send a download information file(e.g., a script file) for upgrading software (in this example firmware)for multiple ECU upgrades with inter-dependencies. OTC client module 301downloads the download information file (e.g., JSON file) from MSP 101and stores the file in local database 308. In an embodiment, topologicalsorting is used to construct hierarchal topologies to ensure softwareupdates are installed in a manner that will preserve softwaredependencies between different software models and/or different versionsof software models. For independent firmware, each install command isexclusive. For dependent firmware, the order of the installationcommands is based on the hierarchal topology using a depth-first search(DFS) algorithm, as described more fully in reference to FIG. 4C.

The update operations proceed as described above for a single ECU. Ifany of the inter-dependent firmware upgrades fail, all relevant firmwarethat is previously installed in the sequence is rolled back. OTC clientmodule 301 will invoke the rollback operation with target versioninformation, via communication manager 304. OTA client module 301 thenreports the appropriate success/fail alerts to MSP 101 separately foreach firmware update/rollback.

In an embodiment, a dedicated controller inside the mobility client isconfigured to manage the OTA update processes, as described in U.S. Pat.No. 9,128,798, for “Module Updating Device,” issued on Sep. 8, 2015,which patent is incorporated by reference herein in its entirety.

Example Data Client Software Architecture

In addition to the OTA client, MSP 101 also supports a data client. Thedata client shares some of the software components of the OTA clientsoftware architecture. The data client perform IoT functions, collectsdata from various devices, sensors and data analytics functions. In anembodiment, the device client employs OMA-DM protocol for clientregistration, MQTT for data exchange and REST APIs for log file uploadsto the Internet. The data client processes server commands and uploadsinstallation logs to MSP 101 using RESTful APIs via OMA-DM or sensordata using MQTT.

Example Delta Compression

In an embodiment, the data sent to a mobility client is compressed usingdelta compression technology. With delta compression, two versions of afile can be compared to determine their differences. The differences areincluded in a differential (“diff”) file or “delta” file. Multiple difffiles can be generated for every software update based on previousversions and available delta compression algorithms. For example,selection of a diff file can be based on a previous version of thesoftware that is available, the file size, the decode time and the deltacompression algorithm that is available. There are several publiclyavailable delta compression algorithms. These delta compression toolsinclude but are not limited to: BSDIFF (bitwise subtraction) and XDELTA3(VCDIFF RFC 3284).

Not every update scenario requires delta compression. Different deltacompression algorithms/tools are suited for different parametersincluding, for example, file size, computation limitations at client,bandwidth requirements, the urgency of the software update, server-sidecomputation limitations and the location of the reconstruction unit. Forexample, in a use case it may be important to roll-out a file to amobility client quickly and bypass delta compression completely. Byconsidering the parameters that are important for a particularapplication (e.g., the automotive industry) an advanced adaptivemethodology for selection can be used where the most appropriate deltacompression tool is selected for delta compression. Equation [1] belowdescribes the selection of a delta compression tool based on a functionF( ):

T _(i) =F(S,O,Δt _(d) ,Δt _(p) ,b,P),  [1]

where S is the reduction in file size, O is the original file size,Δt_(d) is the time to generate the delta file, Δt_(p) is the time toreconstruct the original file, b is the bandwidth restriction and P isthe update or transfer priority. In an embodiment, F( ) can be a linearcombination of weighted parameters, where each of the parameters (S, O,Δt_(d), Δt_(p), b, P) is assigned a weight w_(i) (a number between 0and 1) according to the application as shown in Equation [2]:

T=Σ _(i=1) ^(N) w _(i) F _(i),  [2]

where N is the number of parameters (e.g., N=6) and F_(i) is the “ith”parameter. The value of T that results from application of Equation [2]can be mapped to a look-up table of delta compression tools. Forexample, if T falls within a first range, then the BSDIFF tool isselected, and if T falls within a second range an XDELTA3 tool isselected, and so forth. The weights w_(i) can be determined empirically.

Example Remote Control and Configuration

In an embodiment, a user of MSP 101 can use a configuration panel to adda set of commands to be downloaded to the OTA client or data client. Theuser can define operation commands and data commands using theconfiguration panel. The user (e.g., the SM) can configure a vehiclemodel, device model and IoT device model. To define a vehicle model, theuser adds a vehicle model, make, year, trim and style using a GUI withtext fields for inputting these parameters. In addition, the user canenter a configuration type, a configuration command script and a commandname. To define a device model or IoT device model, the user adds adevice name, manufacturer, description, firmware name, and firmwareversion. In addition, the user can enter a configuration type, aconfiguration command script and a command name.

After the model is made “active” by the user, the configuration valuesset by the SM are downloaded to the mobility client (OTA client ordevice client). The mobility client can either accept, reject orpostpone applying the configuration values. If the mobility clientaccepts the configuration values, the mobility client applies theconfiguration values and sends an “Accept” status to MSP 101 indicatingthat the values were accepted by the mobility client and the status isstored in database (s) 207. If the mobility client rejects or postponesthe configuration values, the mobility client does not apply theconfiguration values and sends a “Reject” or “Postponed” status to MSP101 indicating that the values were rejected or postponed, respectively,by the mobility client and the status is stored in database(s) 207. TheSM can view the status of all the configurations in the SM dashboard.

Example Software Update Planning

In embodiment, software update planning includes five phases: softwareupload, approval, campaign task creation, distribution task creation andinstallation task creation. Each of these phases is the responsibilityof a user class. Hereinafter, “user” refers to a registered MSP 101user. In an embodiment, MSP 101 supports the following user classes(hereinafter also referred to as “roles”) for software update planning:System Administrator, Content Manager, Software Manager, CampaignApprover, Campaign Manager, Deployment Manager and Executive. The SystemAdministrator (SA) manages the creation of users and contacts, approvesor rejects user creation requests, creates or suspends organizations andcreates or deletes roles. The Content Manager (CTM) manages creation ofnew mobility client models and mobility clients (e.g., vehicles, IoTdevices), handles creation of new component models and componentdependency, handles create of new components, manages software modelsand maps software dependencies. The Software Manager (SM) managessoftware uploads and uploads base versions and latest versions tosoftware models. The Campaign Approver (CA) approves or rejects uploadedsoftware versions. The Campaign Manager (CM) creates and managesCampaign Tasks for distribution of software updates based on mobilityclient groups, tracks status of total number of download andinstallation successes and failures. The Deployment Manager (DM) createsand schedules Distribution Tasks, tracks status of Installation Tasksand tracks installation issues. The Executive accesses dashboards andvisualizations (e.g., charts, tables).

Each user is required to register with MSP 101 through a Web portal andprovide authentication information (e.g., user ID and password) duringlogin. In an embodiment, each user class has a dedicated graphical userinterface (GUI) referred to as a “dashboard” that provides static and/orreal-time visualizations (e.g., charts, tables, 2D/3D graphics), andinput mechanisms (e.g., menus, text input boxes, buttons, check boxes,links, etc.) that are tailored to the duties and responsibilities of theuser class. Although the software update planning described aboveutilizes human intervention, in another embodiment the tasks performedby the human users can be completely or partially automated by MSP 101without human intervention.

FIG. 4A is a flow diagram of a planning process 400 for a softwareupdate, according to an embodiment. Process 400 can be implemented usingMSP 101 architecture shown in FIG. 2. Process 400 begins when the SMuploads the software version (401). Upon receipt of the softwareversion, a new Campaign Task is automatically created with “Inactive”status (402). Diff files are then automatically generated for theuploaded software version and “Inactive” status is automatically changedto “Creation” status (403). At this point, the CM can edit the CampaignTask and set the Campaign Configuration using the CM dashboard. Someexamples of edits include a start time and end time of the CampaignTask, and a default DM assignee for the Distribution Task. Because it iscontemplated that there may be multiple DMs, the CM can assign a defaultDM, which can be reassigned later by the CM based on the work load ofthe default DM.

The CM can configure a vehicle grouping (a group of vehicles included inthe Campaign Task) by selecting vehicle parameters (e.g., make, model,year, geography, dealer vs. owner, etc.) from one or more dropdownmenus, then clicking a save button to save the selection. When the CM isdone editing, the CM can start a new campaign by pressing a Start NewCampaign button in the CM dashboard. A new Campaign Task can include asingle update to a single ECU or multiple updates to multiple ECUs, asdescribed in reference to FIG. 3B. In an embodiment, non-criticalupdates are bundled to multiple ECUs that share a same vehicleconfiguration with similar update priority and due dates.

Process 400 continues with two parallel sets of activities. In a firstset of activities, the status of the Campaign Task changes to “Active”(405). When the Distribution Task is scheduled, “Active” status changesto “In-Progress” status. When the end time of the Campaign Task istriggered, “In-Progress” status changes to “Closed” status (407). In asecond set of activities, a new default Distribution Task is created(408). The DM can edit the default Distribution Task and select a newend time (409). When the new end time of the Distribution Task istriggered, no software update is allowed (410).

In an embodiment, the DM can use DM dashboard to create, add or edit aDistribution Task, select a distribution method and creating anInstallation Task. For example, the DM can select time slots to scheduleOTA update appointments with vehicle owners. The DM can enter anapproved update time, a total number of vehicles to receive the updateand an appointment time with the vehicles. The DM can select adistribution method from a list of distribution methods including butnot limited to: Geo Region, Group and Geofence. If Geo Region isselected, the software update will be distributed to the vehiclegrouping in a specified geographic region, such Western United States orNorthern California. If Group is selected, the software update will bedistributed to the vehicle grouping regardless of the locations of thevehicles in the group. If Geofence is selected, the software update willbe delivered to the vehicle grouping for only those vehicles that arewithin a specified geofence. Accordingly, software updates can betargeted, to vehicle groupings, geographic regions and particular areaswithin a geographic region.

FIG. 4B is a diagram illustrating software dependencies between softwaremodels, according to an embodiment. Using a dashboard, a user (e.g., anSM) can add, edit, view, clone, upload, delete and save one or moresoftware models and their dependencies. A software model is defined bysoftware model, software model name, software type (e.g., application),component model, vendor, available software model (e.g., a list ofsoftware models added within the system with base version uploaded) anddependent model. The user can define software compatibility details withdifferent versions of the dependent software model while uploading a newsoftware file for the selected software model.

Referring to FIG. 4B, assume the user adds Software Model A and alsoadds a software dependencies with Software Model B and Software Model C.In this example, Software Model A is installed on ECU 411, SoftwareModel B is installed on ECU 412 and Software Model C is installed on ECU413. This creates a software model hierarchy: Software Model A->SoftwareModel B->Software Model C. Also assume that in some embodiments SoftwareModel B has Versions 1.0, 2.0, 3.0 and 4.0, and that Software Model Chas Versions 1.0, 2.0 and 3.0. While uploading Version 2.0 for SoftwareModel A, the user selects at least one of the versions for SoftwareModel B and/or Software Model C as being dependent on Version 2.0 ofSoftware Model A. MSP 101 checks for software dependencies andcompatibility (if any) for Software Models A, B and C based on thesoftware configuration details sent by the mobility client. If anyupdates are found for multiple dependent software, MSP 101 sends twosets of download links to the mobility client.

A first set of download links includes a single download link to asingle software package that includes updates for all the SoftwareModels A, B and C to be updated. A second set includes download links toindividual software update packages for each software model. Thesoftware installations to be executed on the mobility client are chainedand downloaded in a leaf-to-node hierarchical data structure. Theinstallation sequence executed on the mobility client is based on themodel dependency described by the user in the dashboard.

Referring to the encircled numbers in FIG. 4B, installation manager 303(FIG. 3) on the mobility client first creates installation tasksstarting from the lowest node in the software model hierarchy (3installation tasks for Software Model C—Version 2.0 and Version 3.0).Next installation manager 303 creates an installation task for the nexthigher node in the hierarchy (1 installation task for Software ModelB—Version 1.0). Next installation manager 303 creates an installationtask for the next higher node in the hierarchy (1 installation task forSoftware Model A-Version 2.0). In an embodiment, a topological sortingalgorithm (e.g., Kahn's algorithm) can be run on the mobility client todetermine installation order.

FIG. 5 illustrates a visualization for displaying a map that allows a DMto draw a geofence for software update distributions, according to anembodiment. In this example, dynamic and interactive visualization 500is shown that includes a real-time location 502 of a mobility client onmap 501. Route 503 traveled by the mobility client is displayed as athicker and/or darker line. A user, such as a DM, can use a pointingdevice to draw a geofence region 504 on map 501. When the mobilityclient enters geofence region 504, the software update or a notificationof a pending software update is sent to the mobility client.

Example Issue Detection and Resolution

FIG. 6 is a flow diagram issue detection and resolution, according to anembodiment. MSP 101 provides issue management tools in user dashboardsthat enable users to see a list of issues that is automaticallygenerated by MSP 101 based on incidents that occurred during OTAoperations. Issue management ensures that each issue is identified,analyzed, prioritized and resolved within an appropriate time frame sothat OTA operations are not negatively impacted.

Referring to FIG. 6, process 600 begins when an issue is detected andrecorded (601). For example, the mobility client may report an updatefailure to MSP 101. The issue can be viewed on a dashboard with, forexample, the following screen fields: Issue ID, Issue Name, Issue Type,Issue Category, Reason, Assigned/To, Resolved By, Created Time,Priority, Status and Last Comment. Other embodiments of dashboards mayinclude more or fewer screen fields.

The CM is notified of the issue (602), performs a preliminary analysisof the issue and assigns a priority to the issue based on thepreliminary analysis (603). The priority determines the urgency of theissue so that users know which issue to solve first. In an embodiment, aHigh priority is assigned for critical issues that have a high impact onOTA operations, and have the potential to stop the OTA operationscompletely. A Medium priority is assigned to issues that have anoticeable impact on OTA operations, but will not stop the OTAoperations from performing. A Low priority is assigned to issues that donot affect critical OTA operations, and do not have a negative impact onOTA operations if not resolved immediately.

After assigning a priority, the CM then assigns/forwards the issue toanother user based on the type of issue detected (604). For example, ifit is a deployment problem the SA will assign/forward the issue to theDM. If it is an installation issue, the CM will assign/forward the issueto the DM. If data analysis is required, the CM will assign/forward theissue to a data analyst. The assignee user will be notified of theassignment. The user can view the issue from their dashboard andassign/forward to another user if applicable. The user can also addcomments to a comment section for the issue as a way to recordadditional detail about the issue, and collaborate with other users.

Once assigned the assignee will perform a more rigorous investigation ofthe issue and provide a diagnosis (605). The diagnosis is applied and ifthe issue is resolved (606) the issue is closed (607). After the issueis resolved, the user can change the status field of the issue from “InProgress” to “Resolved.”

Example Data Analysis Tools

FIG. 7 illustrates a data analyst dashboard 700, according to anembodiment. Dashboard 700 can be invoked on MSP 101 by a data analyst.Dashboard 700 includes function bar 701, tab control 702, tool set 703and workflow canvass 704. Dashboard 700 enables the data analyst tocreate a distributed workflow by dragging 'n dropping workflow objectslisted in tool set 703 onto workflow canvass 704, configure propertiesand perform mappings for the workflow objects (See FIGS. 8A-8F), savethe workflow, run the data workflow, deploy the workflow and select andload previously added workflows.

In the example shown, distributed workflow 705 for fraud detection wascreated and saved by the data analyst, as indicated by the frauddetection tab of tab control 702. Each workflow created by the dataanalyst can be saved as a tab with a user-specified name so that it canbe accessed later using tab control 702. Function bar 701 includesvarious functions including Add, Load, Save, Run and Deploy. In theexample shown, the workflow named Fraud Detection has been loaded.

Workflow 705 includes data source objects 706 a, 706 b, which in thisexample represent two relational database management systems RDBMS-1 andRDBMS-2. Data source objects 706 a, 706 b feed into a first join object707 a, creating a single stream of data, which is then output to filterobject 708. Filter object 708 is configured to filter out data from thedata stream. The output of filter object 708 feeds into a second joinobject 707 b and executor object 709, which executes an algorithm or astate machine selected or developed by the data analyst. The output ofexecuter object 709 feeds into visualization object 710, which generatesa chart or table of the workflow output.

Some examples of data sources include but are not limited to: relationaldatabases, No SQL-based databases (e.g., Cassandra™+Elasticsearch™) andmessage streams (e.g., Kafka® message stream). The properties editor tab801 of a data source editor shown in FIG. 7 is used to configureproperties of a data source, as described more fully in reference toFIG. 8A.

Some examples of filters include but are not limited to: range filtersthat limit data to a range of contiguous values, date filters that usecalendar controls to adjust time or date selections to select a singlecontiguous range of dates or use a date range filter to exclude dateswithin a specified range and expression filters. Expression filters letthe data analyst define more complex filters using SQL expressions.

Some examples of transformation include but are not limited to: a sumtransformation that sum up values having numeric data types in a columnof a database schema, and a join transformation that provides an outputthat is generated by joining two datasets using a FULL, LEFT or INNERjoin.

Some examples of executors include but are not limited to algorithms andstate machines. In the example shown, a decision tree algorithm wasselected to classify categorical data based on attributes. Any desireddata mining, machine learning and statistical learning algorithms can beused as an executor, including but not limited to: simple linearregression, multiple linear regression, decision tree regression, randomforest regression, gradient boosted tree regression, logisticregression, polynomial regression, linear discriminant analysis (e.g.,Bayes' Theorem), resampling methods, support vector machines,unsupervised learning, supervised learning (e.g., neural networks).

Some example of data sinks include but are not limited to: relationaldatabases, No SQL-databases, visualizations (e.g., charts, scatterplots, 3D mesh surfaces) and tables. When the data sink is avisualization the data analyst can view the workflow output in the formof visualizations or in the form of tables.

FIG. 8A illustrates properties editor tab 801 of a data source editor,according to an embodiment. Properties editor tab 801 allows the dataanalyst to specify a resource, resource type, host name, port number,connection string, user name, password, and then select a data schemafrom a plurality of data schemas. FIG. 8B illustrates map editor tab 802of the data source editor, according to an embodiment. When the dataanalyst selects map editor tab 802, the data analyst can select adatabase schema, tables and columns.

FIG. 8C illustrates properties editor tab 803 of a filter editor,according to an embodiment. The data analyst can use properties editortab 803 to select a filter (e.g., range, date or expression). In thisexample, a filter expression is selected. FIG. 8D illustrates map editortab 804 of the filter editor, according to an embodiment. The dataanalyst can use map editor tab 804 to set up the filter expression usingconditional logic expressions. In the example shown, the data analystcreated the conditional expression: if [Column 1]<10000 then “True” else“False.” This filter expression will cause the filter output to be“True” if there are more than 10,000 items in Column 1 of a databasetable. Otherwise, the filter output will be “False.”

FIG. 8E illustrates properties editor tab 805 of a transformationeditor, according to an embodiment. The data analyst can use propertieseditor tab 805 to select a transformation (e.g., sum, join). FIG. 8Fillustrates map editor tab 806 of the transformation editor, accordingto an embodiment. The data analyst can use map editor tab 806 to set upa join expression. For example, the data analyst can select columns fromthe respective tables and drop them in either “Left Hand Side” or “RightHand Side” to make a join.

Example Data Mining Tools

FIG. 9A is a flow diagram of data mining process 900 for generating amodel using a machine learning algorithm, according to an embodiment.Data mining is the process of extracting hidden and useful informationfrom raw data by utilizing statistics, Artificial Intelligence (AI),machine learning (ML) techniques and smart algorithms. It also involvesfinding frequent patterns, associations and correlations among the dataelements using ML algorithms.

Referring to FIG. 9A, data from data warehouse 901 (e.g., No SQL-baseddatabase) or data uploaded by system clients 902 are input into dataanalytics pipeline 903. Data analytics pipeline 903 can be implementedusing an Apache Spark™ data pipeline architecture, and the input datacan be in comma-separated value (CSV) format.

Data analytics pipeline 903 includes data mining algorithms 904, machinelearning algorithm 905, model 906 and file system 907 (e.g., a Hadoopdistributed file system). The input data is processed by machinelearning algorithm 905 using one or more data mining algorithms 904. Theoutput of machine learning algorithm 905 is model 906, which is saved infile system 907. Model 906 is then used by stream processing module 909to process message stream 908 (e.g., a Kafka™ message stream) to providepredictions 910 (e.g., predictive maintenance).

In the example shown, data mining algorithms 904 are regressionalgorithms, including decision tree regression, random forest regressionand gradient boosted tree regression, respectively. Machine learningalgorithm 905, however, can utilize any known data mining algorithm,including but not limited to: decision trees, naïve Bayes'classification, ordinary least squares regression, logistic regression,support vector machine (SVM), clustering algorithms (e.g., k-means),principal component analysis (PCA), singular value decomposition (SVD),independent component analysis (ICA), etc. In an embodiment, the machinelearning algorithm is implemented using Apache Spark ML™.

FIG. 9B is a flow diagram of a process for training and evaluating model906, according to an embodiment. Supervised data can be retrieved fromfile system 912 and input into feature extraction module 913. Featureextraction module 913 selects relevant features (variables, predictors)for use in constructing model 906. A percentage (e.g., 20%) of thefeatures form test dataset 914 and the remaining features (e.g., 80%)form training dataset 915. Training set 914 is input into machinelearning algorithm 905 to train model 906. The output of model 906 isinput into model evaluation module 918, where it is evaluated againsttest dataset 914. If a desired accuracy is reached 919, model 906 issaved in file system 920. If a desired accuracy is not reached 919, theparameters of model 906 are adjusted, and the output of model 906 isevaluated again by model evaluation module 918.

Example Predictive Maintenance

FIG. 10A is a flow diagram of a predictive maintenance process for avehicle brake system, according to an embodiment. A vehicle owner/driverneeds to know when to replace the brake pads or brake fluid so that theydo not have an accident or destroy the brake rotor, which is the metalpart of the brake assembly. If they wait too long the brake pad willdestroy the rotor as metal rubs up against metal. The driver cannot beexpected to check the brakes each time the vehicle has stopped for brakepad wear and fluid levels. And if the driver changes the brake pads orbrake fluid based on some preset schedule then they may be wasting moneybecause they might be changing the brake pads and fluid too often. It ispreferred to implement a model that can predict when the brake pads orbrake fluid should be changed.

Predictive maintenance techniques area designed to help determine theconditions of in-service mobility clients to predict when maintenance,such as brake system service, should be performed. Predictivemaintenance provides cost savings over routine or time-based preventivemaintenance at least because maintenance is performed only whenwarranted. Predictive maintenance employs monitoring and predictionmodeling to determine the condition of a mobility client and to predictif a component is likely to fail and when.

In the example shown, the mobility client is a vehicle and it is assumedthat the vehicle is equipped with an Onboard Diagnostic (OBD) device andthat the OBD device streams real-time data. The real-time data is sentthrough an OBD port and includes vehicle speed, revolutions per minute(RPM), air temperature and readings from various sensors.

Referring to FIG. 10A, brake pad sensor data 1001, brake fluid sensordata 1002 and other sensor data 1003 (e.g., speed, mileage, RPM) aresent to OBD device 1004 installed on the vehicle. OBD device 1004aggregates the sensor data (1005) and sends it to MSP (1006). Brake padsensor data 1001 is input into a data analytics pipeline created by adata analyst as described in reference to FIGS. 7 and 8, which predictsa life expectancy of the brake pads (1008). A severity level (e.g.,High, Medium, Low and Optimal) for the brake pad wear is determinedbased on the prediction results and a defined state flow (1009).Similarly, brake fluid sensor data 1001 is input into the data analyticspipeline, which predicts a brake fluid change time (1010). A severitylevel for the brake fluid level is identified based on the predictionand a defined state flow (1011). The sensor data, predictions andseverity levels are stored with other vehicle details in a datarepository (1012). Based on the severity level, operation engine 204(FIG. 2) can generate one or more visualizations and/or alerts (visualor audio) to send to the vehicle or to a system client (e.g., an OEM).The visualizations and/or alerts can be presented to the user on avehicle dashboard as a warning light, displayed on computer screenand/or played through a loudspeaker. Some examples of alerts include“Time to Failure” and “Recommended Maintenance Time.”

FIG. 10B is a more detailed flow diagram of a predictive maintenanceprocess for a brake system, according to an embodiment. Brake pads arethe component in the braking system that provides the friction to slowthe vehicle. They are located in the brake calipers and function bypressing against the disc brake rotors with friction material to slowand stop the wheels. As they are a contact wear item, the brake padswill eventually wear out over time and need to be replaced.

Predicting Brake Pad Life Expectancy

To compute brake pad life expectancy, we assume that values below aresent to MSP 101 as part of the OBD data.

-   -   Force on Brake Pads=500 N (System to calculate average force of        brake pads)    -   Force applied by the brake pad on the rotor (F)=93.545 KN    -   Surface area of contact of brake pad (A)=5445.414 mm    -   Pressure Applied on the brake pad (P)=F/A=93.545/5445.414=17.178        Mpa    -   Coefficient of friction between rotor and pad (μ)=0.45    -   Braking torque applied by the pad on the rotor (T)=5519.188 Nm    -   Radial distance of point of application of equivalent force        (R)=131 mm    -   Modifying factor dependent on motion type, load a speed (F1)=1.8    -   Environmental factor, accounts for temperature & cleanliness        conditions (F2)=1    -   Wear factor (K)=1.3*10 mm/Nm    -   Number of rotations under maximum load braking which will lead        to 1 mm of pad thickness to wear out (ΔT)=1 mm

Based on the above values, the system will compute the wear thickness ΔTaccording to Equation [3]:

ΔT=F1*F2*K*PXR*2πN   [3]

By applying the above values to Equation [3], we get N=30000 rotations,which means the brake pads will last for more than 30 thousand rotationsunder full load braking (500N). Now let's assume the values below arealso sent as part of the OBD data.

-   -   Diameter of the wheel (D)=547. mm    -   Circumference of wheel (C)=1.7 m    -   Speed of the Vehicle (S)=50 KMPH    -   Braking distance of a car from 50 kmph to zero (B)=4 meters    -   Number of rotations per braking, n=B/c=4/1.7=2.35=2.5 (Round        off)    -   No. of times brakes applied per kilometer=2

Based on these additional values, the life of the brake pad L is givenby Equation [4]:

L=N/(2*n).   [4]

Applying the values above (30000/2*2.5)=6000 KMS of vehicle travel. Nowthat we know the life expectancy of the brake pads, we can now definethe state flows and their related alerts as shown in Tables 1 and 2,respectively.

TABLE 2 State Flow Definition Life Expectancy State State State StateTypes (KMS) 1 2 3 4 Brake 6,000 (per prediction) >2500 1500-2500500-1500 <500 Pads

TABLE 3 Severity and Alerts State State Flow Name/Severity Alert State 1Optimal No Alert State 2 Low No Alert State 3 Medium No Alert State 4High Alert

Based on the usage of the brake pads, the life expectancy of the brakepads may move back and forth across states. An occurrence is the totalnumber of times the states toggle between Optimal to High and High toOptimal, as shown in the Example of Table 3.

TABLE 3 Occurrence Example No. of Times State Severity Identified State1 Optimal 2 State 2 Low 4 State 3 Medium 6 State 4 High 8 Total Numberof Occurrences 20

The “time to failure” of the brake pads specifies the time remaining forthe complete failure of the part or the component. Time is specified indays and it is predicted based on the average kilometers/miles drivenfor each day. For example, if the life expectancy of the brake pads ispredicted as 6000 KMS, and the average kilometers driven in a day is100, then the recommended maintenance time will be 6000/100=60 days.Table 4 shows recommended maintenance times for this example.

TABLE 4 Recommended Maintenance Times (Brake Pads) Recommended Time toFailure Maintenance Time  <30 days Immediate 30 days and <60 days Innext 15 days 60 days and <90 days In next 30 days >=90 days NA

Predicting Brake Fluid Change

Brake fluid is a type of hydraulic fluid used in hydraulic brake andhydraulic clutch applications in automobiles, motorcycles, light trucks,and some bicycles. It is used to transfer force into pressure, and toamplify braking force. In regular maintenance activities, fluid ischanged based on the number of kilometers/miles driven but not on theviscosity of the fluid. Through predictive maintenance, actual viscositylevel of the brake fluid can be monitored and change of brake fluid canbe predicted. Viscosity index and classification is shown in Table 5below:

TABLE 5 Viscosity Index and Classification Viscosity IndexClassification Under . Low 35 to 80 Medium 80 to 110 High Above 110 VeryHigh

Too low of viscosity may cause increased metal-to-metal contact,increased friction and wear, increased oil consumption and leakingseals. Too high of viscosity may cause increased fluid friction,increased operating temperatures and reduced energy efficiency. Tables 6and 7 provide a state flow definition and severity and alerts,respectively, for brake fluid.

TABLE 6 State Flow Definition Life Expectancy State State State StateTypes (KMS) 1 2 3 4 Brake NA <35° C. 35-80° C. 80-110° C. >110° C. Fluid

TABLE 7 Severity and Alerts State State Flow Name/Severity Alert State 1Optimal Low Viscosity State 2 Low No Alert State 3 Medium High Viscosity

Based on the usage of the brake, the life expectancy of the brake fluidmay move back and forth across states. An occurrence is the total numberof times the states toggle between Low to High and High to Low, as shownin the Example of Table 8.

TABLE 8 Occurrence Example No. of Times State Severity Identified State1 Low 2 State 2 Medium 4 State 3 High 6 Total Number of Occurrences 12

The recommend maintenance time specifies the predicted time or distance(KMS) remaining for the complete failure of the part or component. Table9 shows recommended maintenance times for this example.

TABLE 9 Recommended Maintenance Times (Brake Fluid) Recommended StateSeverity Maintenance Time State 1 Low Immediate State 2 Medium NA State3 High Immediate

FIG. 10B is a more detailed flow diagram of a predictive maintenanceprocess for a vehicle brake system, according to an embodiment. When thevehicle is running (1014) and brakes are applied (1015), brake pad lifeexpectancy is predicted (1016) based on sensor data 1017. Sensor data1017 includes but is not limited to: speed of the vehicle, pressureapplied on the brake pads, force applied on the brake pads, number ofrotations per braking and number of times brakes are applied per KM.Failure analysis is then performed on the predicted remaining brake padlife X to determine the state of the brake pads (1018). If there is ahigh risk state then urgent maintenance is required (1019), and a reportincluding the failure details is generated (1020) and/or an alert issent to the operator of the vehicle, a system client or other interestedparty with authority to access the report or receive the alert.

When vehicle is running (1014) and brakes are applied (1015), brakefluid viscosity is calculated (1021) based on sensor data 1022. Sensordata 1022 includes but is not limited to: temperature and fluid density.Failure analysis is then performed on the calculated viscosity Y todetermine the state of the brake fluid (1023). If there is a high riskstate than urgent maintenance is required (1024), and a report includingthe failure details is generated (1025) and/or an alert is sent to theoperator of the vehicle, a system client or other interested party withauthority to access the report or receive the alert.

The predictive maintenance described above for a single vehicle brakesystem can be applied to any vehicle component that is subject tofailure or that requires replacement, such as tire pressure, balance,engine (e.g., spark plugs), transmission, battery, fuel system, fuelfilter, cooling system, power steering fluid, oil change, oil filter,etc. Additionally, the predictive maintenance processes described abovecan use sensor data from a vehicle group rather than a single vehicle.For example, sensor data from a plurality of vehicles in a vehicle group(e.g., vehicles that have the same make, model, year, style and trim)can be maintained in a data repository and used to predict maintenancefor the vehicle group and to establish statistical trends and baselinesfor the vehicle group. For self-driving vehicles, the predictivemaintenance processes can predict when sensors (e.g., stereo camera,LiDAR, Radar, Sonar, accelerometers, gyroscopes) are failing or need tobe recalibrated. The prediction maintenance processes can also beextended to predicting occurrences of events not related directly tovehicles, such as predicting a traffic jam based on sensor datacollected from crowdsourcing a large number of vehicles that aretraveling on a particular road or highway. Such predictions can be usedto generate map tiles with traffic overlays (e.g., color coding routes),which can be sent to mobility clients when the mobility clients enter aspecific geographic region or geofence (see, FIG. 5)

FIG. 10C is an example predictive maintenance report 1014, according toan embodiment. Report 1014 includes multiline chart 1015 of real-timesensor data, such as brake pad force, brake pad pressure, temperature,brake fluid level and hydraulic pressure. To the right of char 1015 isbrake details 1016 including break pad life expectancy, current mileage,severity, brake location, occurrence, time-to-failure and recommendedmaintenance time. A graphic of the vehicle visually indicating thecondition of each brake using a color coding scheme to indicateseverity. At the bottom of report 1014 is occurrence history table 1017including the occurrence number, severity, time stamp, time-to-failureand recommended maintenance time.

FIG. 11A is a block diagram of claims management system 1100, accordingto an embodiment. Fraudulent activity costs the insurance industrybillions of dollars annually and is a major reason why premium costscontinue to increase, particularly in geographic areas prone tofraudulent risk. Whether it is “hard fraud” (e.g., staged accidents) or“soft fraud” (e.g., embezzlement), there are always indicators thatsuggest the potential for a high-risk claimant. These indicators areoften subtle but, if discovered, can have a significantly positiveimpact on an insurer's bottom line. In this example, we assume eachvehicle is equipped with an OBD device that can stream real-time data,and the insurance provider will send the claim details through theservice API (See FIG. 2).

Vehicle 1101 streams sensor data which is saved into data repository1102 (e.g., Cassandra™ database+Elasticsearch™) and historical sensordata is maintained. In a claims management dashboard (not shown), claimsmanager 1103 uploads the claim details to claims management system 1100in the form of a .csv file (1104). The claim files uploaded are listedin the claims management dashboard. Based on the claim details uploaded,the brake related historical data for vehicle 1101 is retrieved fromdata repository 1102. An analysis is performed on the retrievedhistorical data (1104) to determine if the claim is fraud or genuine(1105). If the claim is determined to be genuine, no record is createdin the claims management system (1106).

If the claim is determined to be fraud, the system maintains the claimdetails in the claim management system (1107). Claims manager 1103 isnotified about the fraudulent claim 1108 (e.g., through e-mailnotification, alert notification in the dashboard). Claims manager 1103logs into claims management system 1100 and views the claim details(1109).

In the dashboard, the claims manager views the fraud analysis eitherapproves or rejects the claim 1110). Based on the reason provided, theclaims manager will decide to approve or reject the fraud (1111). Ifrejected, the claim details will be maintained in data repository(1112). If approved, claims manager 1103 downloads a fraud verificationreport 1114 with details of the fraud analysis (1113), and sends report1114 to the insurance provider.

FIG. 11B is an example fraud verification report 1114, according to anembodiment. The fraud verification report described below is an example.Other formats can be used with more or viewer sections and details,including the additional of one or more visualizations (e.g., charts).

Report 1114 includes table 1115 listing the fraud claim records by claimID, claim type, claim date and time and status (e.g., open, approved,rejected). When the claims manager clicks on a fraud claim record intable 1115, the details of the record populate the fields in indicatorand readings table 1116. Table 1116 includes indicators (e.g., brakepedal force, deceleration peak, left to right brake imbalance, etc.).The actual readings, minimum readings, maximum readings and status(e.g., low, high, optimal) included in columns of table 1116. Certainkey indicators 1117 are displayed prominently in report 1114 (e.g.,engine load, engine temperature, RPM, speed). Section 1118 of report1114 includes text fields for the claims manager to enter the result ofher fraud analysis, and a comment section to enter her reasons for theresult. In an embodiment, the fraud analysis is automated without userintervention. Section 1119 of report 1114 includes a comment section toenter rejection comments and buttons to approve or reject report 1114.Section 1120 of report 1114 displays a claim history including date,claim ID, claimant, claim (e.g., brake failure), fraud analysis result(e.g., Yes or No) and status (e.g., approved, rejected).

FIG. 12 is a flow diagram of process 1200 for using social data forvehicle management, according to an environment. Social data refers todata individuals create that is knowingly and voluntarily shared by themon social medium platforms. Monitoring online customer conversationsover social media like Facebook® and LinkedIn® can be used to capturefirst-hand information about the user, which can then be used togenerate meaningful insights.

Process 1200 begins by selecting key words from social media (1201). Forexample, domain experts analyze a concept to be studied and identifyappropriate key words and phrases related to the concept (e.g.,synonyms/morphologic variants) for extraction key words from socialmedia streams (e.g. Tweets) to create an analytic corpus of key words.For example, a concept can be vehicle models and the key words could beChevy Sonic™, Ford Fiesta™, Honda Fit™, etc.

Process 1200 continues by importing data from the social media (1202).For example, the last N Tweets (e.g., 200) can be imported by searchingthe selected key words and phrases via Tweet import tools.

Process 1200 continues by preparing the data (1203) by removingextraneous symbols and words. For example, quotes, commas and semicolonscan be removed from the data. Also, links can be removed (e.g., URLs).The cleaned Tweets can then be grouped based on vehicle models.

Process 1200 continues by analyzing the data (1204) by comparing thedata with the key words and extracting words that match the key words.For example, a Tweet that reads #bought a new car “Chevy Sonic™” and I'mloving it# would match the key phrase “Chevy Sonic.”

Process 1200 continues by searching for owners of vehicles that matchthe keywords or phrase and sends the owners a communication (e.g.,email, text message, Tweet) that includes or quotes the Tweets matchingthe key words or phrase. For example, an email can be sent that reads:

-   -   “Hi [name],        -   Below is a list of Tweets posted by people who own a Chevy            Sonic™            -   [Tweet 1]            -   [Tweet 2],            -   [Tweet 3].”

Example Security Processes

FIG. 13 illustrates security process 1300 for software update packagedistribution, according to an embodiment. Security is a critical concernfor OTA services. The server, the client and the downloaded softwarepackage need to be authenticated to avoid man-in-the-middle attacks. Thedefault URL of MSP server 1303 is provided to mobility client 1301 andthe root certificate is handled by HTTPS. To authenticate mobilityclient 1301, private key 1306 of mobility client 1306 is stored withmobility client 1306 and public key 1307 of mobility client 1301 isstored on MSP server 1303.

A message sent by mobility client 1301 contains an encrypted sessiontoken that it received from MSP server 1303 during a registrationprocess. The message can be sent using JSON format. The session token israndomly generated by MSP server 1303 and it is unique per session. Thesession token is encrypted by mobility client 1301 with private key 1306of mobility client 1301. MSP server 1303 decrypts the session token withpublic key 1307 of mobility client 1301, and if the session tokenmatches the session token sent to mobility client 1301 during theregistration process, mobility client 1301 is validated. The sessiontoken has an expiration date. If the session token is expired, mobilityclient 1301 requests a new session token from MSP server 1303.

Software package 1304 stored in data repository 1302 is encrypted withfile download key 1305. File download key 1305 is sent to mobilityclient 1301 as part of the download information. Mobility client 1301decrypts file download key 1305. Mobility client 1301 decryptsdownloaded software package 1304 with file download key 1305.

Example Home Page with Visualizations

FIG. 14 is an example home page including a variety of visualizations,according to an embodiment. Home page 1400 may be used by and SA to geta complete overview of OTA operations through visualizations (e.g., linecharts, bar charts, pie charts).

In the example shown, home page 1400 includes Task line chart 1401showing the number of active Campaign Tasks, Distribution Tasks andInstallation Tasks over a user-specified time period. Below Task linechart 1401 is Installation Task line chart 1402, showing the status ofInstallation Tasks over the user-specified time period. BelowInstallation Task line chart 1402 is Instance bar chart 1403 showing thenumber of instances of each engine (e.g., transformation engine,intelligence engine, message engine, operation engine) in MSP 101 togive an overview of the system load. Next to Instance bar chart 1403 isIssue pie chart 1404 showing a breakdown of issues by percentages. Nextto Issue pie chart 1404 is Distribution Task pie chart 1405 showingDistribution Task details in percentages, such as the percentage ofoffered, to do, no update, implementation in progress, installed,download in progress, downloaded, rejected, failed and postponed.

Home page 1400 described above is an example and other embodiments mayinclude more, fewer or different visualizations. For example,visualizations can include 3D charts (e.g., mesh plots), animations,horizontal bar charts, scatter plots, histograms, etc. There can also betext and audio reports. Home page 1400 can be configured based on thesize and resolution of the display screen. In an embodiment, eachvisualization can be manually resized and/or moved around the displayscreen by the user to create a customized screen.

Example Mobility Client Architecture

FIG. 15 is a system block diagram of a self-driving vehicle system 1500,according to an embodiment. System 1500 is one example of a systemtopology and other systems may contain more or fewer components orsubsystems.

System 1500 includes wireless transceiver (TX) 1501, microcontrollerunit (MCU) 1502, peripheral interface 1503, input/output devices 1504,system memory 1505, data storage 1506, engine system 1516, steeringsystem 1518 and brake system 1519. In some exemplary embodiments, system1500 optionally includes one or more of OTA/device clients 1507,autopilot controller 1508, stereo camera 1509, LiDAR 1510, GNSS receiver1511, attitude and heading reference system (AHRS) 1512, wheelencoder(s) 1513, vehicle controller(s) 1514, servocontroller(s)/drivers(s) 1515, engine system 1516 and quad encoder 1517.All of these system components are coupled to one or morebuses/networks, including but not limited to: a CAN bus, FlexRay™ andEthernet. Different types of busses/networks (e.g., that use differentbus/network protocols) can be coupled together using gateways that cantranslate or map between the different bus/network protocols.

Wireless TX 1501 includes a radio frequency (RF) transmitter andreceiver for transmitting and sending messages using OTA serviceprovisioning (OTASP), OTA provisioning (OTAP) or OTA parameteradministration (OTAPA). Wireless transmissions (e.g., messages) can besent and received using any known wireless communication protocol,including but not limited to: OTA, OMA-DM, 2G, 3G, 5G, IEEE 802.15.4,WiFi, ZigBee, Bluetooth, etc.

MCU 1502 is a centralized processor that is coupled to wireless TX 1501and is responsible for accessing data stored in system memory 1505 anddata storage 1506 through peripheral interface 1503. Peripheralinterface 1503 can include one or more I/O controllers and are coupledto input/output devices 1504, including display screens (e.g., LCD, LED,OLED, touch screens, surfaces or pad), pointing devices, switches,buttons and any other mechanical or virtual input/output mechanisms. TheI/O controllers are configured or programmed to send and receivecommands and/or data (unidirectional or bi-directional) over synchronousor asynchronous buses/interfaces, including full-duplex, half-duplex,master/slave, multi-master, point-to-point or peer, multi-drop andmulti-point buses/interfaces.

In an embodiment, system memory 1505 (e.g., RAM, ROM, Flash) storesinstructions to be executed by MCU 1502, including an operating systemand applications. System memory 1505 store OTA/device clients 1507 forfacilitating OTA service, as discussed in reference to FIGS. 3A and 3B.

MCU 1502 is coupled to autopilot controller 1508. Autopilot controller1508 is responsible for receiving and processing sensor data from stereocamera 1509, LiDAR/Sonar 1510, GNSS RX 1511, AHRS 1512, and generatingautopilot commands based on analysis of the sensor data. AHRS 1512includes sensors on three axes that provide attitude and headinginformation for the vehicle, including roll, pitch and yaw in a suitablereference coordinate frame. The sensors can be either solid-state ormicroelectromechanical systems (MEMS) gyroscopes, accelerometers andmagnetometers. In an embodiment, a non-linear estimator, such as anExtended Kalman filter is used to compute a navigation solution fromdata provided by AHRS 1512. Other sensors can be included in system 1500including but not limited to temperature sensors, pressure sensors andambient light sensors.

GNSS RX 1511 can be any satellite-based positioning system receiver,including but not limited to: Global Positioning System (GPS), GLONASS,Galileo and BeiDou. Other position technologies include WiFi positioning(e.g., using WiFi fingerprint database and Bayes' predictor) and celltowers (e.g., using trilateration, time of flight (TOF), angle ofarrival (AOA)).

Autopilot controller uses various AI technologies for routing andcollision avoidance. An example technology is Bayesian Simultaneouslocalization and mapping (SLAM) algorithms, which fuse data frommultiple sensors and an off-line map into current location estimates andmap updates. Another example, is SLAM with detection and tracking ofother moving objects (DATMO), which also handles cars and pedestrians.Less complex systems may use roadside real-time locating system (RTLS)beacon systems to aid localization. Another example is visual objectrecognition using machine vision, including deep learning or neuralnetworks having multiple stages or levels in which neurons are simulatedfrom the environment that activate the network. The neural network canbe trained on data extracted from real life driving scenarios. Theneural network is activated and “learns” to perform the best course ofaction based on the driving scenarios.

Vehicle controller(s) 1514 can include TCUS, ECUs and any controllerthat is used to control a vehicle subsystem. Vehicle controller (s) 1514receives command from autopilot controller 1508 and generates controlcommands to server controller(s)/driver(s) 1515 for controlling/drivingvarious vehicle subsystems, including but not limited to: engine system1516, steering system 1518 and brake system 1519.

In addition to sensors, system 1500 includes various encoders, includingquad encoder 1517 for measuring steering angles and wheel encoder 1513for measuring wheel rotations. Encoders 1513, 1517 provide output toautopilot controller 1508 to be factored into is computations.

FIG. 16 is a system block diagram of a distributed computingarchitecture for a vehicle, according to an embodiment. System 1600includes TCU 1602, and ECUs 1601, 1605 coupled to Ethernet 1603.Ethernet 1603 is coupled to CAN bus 1606 by gateway 1604. CAN busincludes ECUs 1607, 1608 and 1609. System 1600 is an example topologyand other topologies can include more or fewer ECUs, gateways andbusses.

An ECU can control a series of actuators on an internal combustionengine to ensure optimal engine performance by reading values from anumber of sensors within an engine bay, interpreting the values usingmultidimensional performance maps (e.g., lookup tables), and adjustingthe engine actuators based on the interpreted values. Each ECU includesa microprocessor which processes the inputs from various sensors (e.g.,engine sensors) in real-time. An ECU contains hardware includingelectronic components on a printed circuit board (PCB). The maincomponent on the PCB is a microcontroller unit (MCU) or centralprocessing unit (CPU). The software/firmware is stored in the MCU/CPUcache or other memory chips on the PCB, such as EPROM or flash memory.In an embodiment, the MCU/CPU can be reprogrammed (reflashed) bydownloading updated code using OTA operations as described in referenceto FIGS. 2 and 3. CAN bus 1606 allow TCU 1602 and ECUs 1601, 1605, 1607,1608, 1609 to communicate with each other without a host computer usinga message-based protocol. Gateway 1604 is also an ECU used inside thevehicle to translate traffic from one vehicle bus to another, such asbetween CAN bus 1606 and Ethernet 1603. Gateway 1604 can provideconnectivity to a number of different types of buses, including CAN,LIN, and FlexRay®. Gateway 1604 translates data between multiple busesof the same protocol or translates to different types of buses to ensurethat the TCU and ECUs have the right information they need for thevehicle to operate as expected.

Alternative OTA Update Methods 1. OTA Update Method Using SIM/eUICC Card

Subscriber Identity Module (SIM) cards and embedded universal integratedcircuit card (eUICC) (e.g., soft SIM, eSIM) are typically included in acellular modem installed in vehicles for connectivity. Modern SIM/eUICCcards include a smart card operating system (e.g., JavaCard® OS) thatcan be used to run a virtual machine (VM) for small applications, and tostore cellular profiles, applications, etc. An OTA client applicationcan be installed on the eUICC for JavaCard® can be used for basic OTAbootstrapping, and can be accessed from a host CPU in the vehiclethrough, for example, an application programming interface (API). TheOTA client application can also store a user and/or device identitydata. For example, a eUICC subscription management system can load ormodify user and/or device identify data in the eUICC. The user and/ordevice identity data can be accessed by a host application running onhost computer through an API.

In an embodiment, an apparatus that performs OTA updates using aSIM/eUICC card comprises: a wireless modem configured to establish awireless communication link with a network; an integrated circuit modulesecurely storing a mobile subscriber identity and a related key, whichare used to identify and authenticate a subscriber on the apparatus;memory embedded in the integrated circuit module storing instructionsfor an operating system, a virtual machine and an over-the-air (OTA)client software; one or more processors executing instructions of theoperating system, virtual machine and OTA client software to: establisha communication session with an OTA mobility services platform; andperform, during the communication session, one or more OTA operationswith the OTA mobility services platform.

In an embodiment, the memory stores an identifier for the apparatus. Inan embodiment, the one or more OTA operations comprise: receiving abroadcast or multicast of one or more images or files, where each imageor file includes a vehicle identifier; obtaining, from the OTA mobilityservices platform, cryptographic data (e.g., encrypted hash) of theimage or file; and verifying, using the cryptographic data, the image orfile.

2. OTA Update Method Using Home/IoT Gateway

In an embodiment, a smartphone application acts as a proxy serverbetween the OTA distributed computing platform and the OTA client in thevehicle. For example, the application can connect to the vehicle througha wireless connection (e.g., Bluetooth connection) or a physical port(e.g., a USB port) to obtain vehicle data (e.g., VIN, ID, softwareversions). The smartphone then connects to the OTA distributed computingplatform through a home gateway (e.g., a WiFi router in the user's home)and obtains the OTA update (e.g., a software update). At a later time,convenient to the user, the user connects the smartphone to the vehicleagain and performs the OTA update on the vehicle software. In anembodiment, the vehicle connects to the user's home gateway through awireless connection (e.g., WiFi connection) and controls the OTA updateprocess through the smartphone application that connects to the homegateway.

In an embodiment, an OTA update method that uses a home or IoT gatewaycomprises: establishing, by one or more processors of a mobile device(e.g., mobile phone, thumb drive), a first communication session (e.g.,a Bluetooth, WiFi session, USB connection) with a vehicle computer(e.g., an automobile computer or TCU or ECU); receiving, through thewireless connection, a vehicle identifier associated with the vehicle(e.g., a VIN) and data identifying code or data installed on the vehicle(e.g., software ID and/or version number); establishing, by the one ormore processors, a second communication session with an over-the-air(OTA) mobility services platform (e.g., through a home gateway);receiving, during the second communication session and using the vehicleidentifier, download information from the OTA mobility services platform(e.g., URL to retrieve a software/firmware update from a network-basedstorage device); retrieving, by the one or more processors using thedownload information, an update to the code or data (e.g., a diff filefor updating firmware/software files); storing, by the one or moreprocessors, the update in a storage device accessible by the mobiledevice; establishing, by the one or more processors, a thirdcommunication session with the vehicle (e.g., a Bluetooth, WiFi session,USB connection); and transferring, during the third communicationsession, the update to the vehicle. In an embodiment, the secondcommunication session is established through a wireless local areanetwork. In an embodiment, the first and third communication sessionsare IEEE 802.11x, IEEE 802.15.x, near-field communication (NFC) orBluetooth™ communication sessions. In an embodiment, the first and thirdcommunication sessions are established through a communication port(e.g., USB port) of the vehicle computer.

3. OTA Update Method Using Broadcast or Multicast

In an embodiment, the MSP provides a continuous broadcast or multicastdigital signed OTA images (e.g., delta files) to mobility clients inblocks with IDs. The mobility clients collect all or part of an imagebased on the ID and establish communication with the MSP to obtain anencrypted hash of the image to authenticate the image signature. Theusual communication with the MSP (e.g., registration, offering,signature, etc.) is required bit is very short in duration. In anembodiment, the broadcast or multicast can be done over a cellularnetwork (e.g., using an instant messing service (IMS)). In anembodiment, an additional or complementary broadcast network can beused, including but not limited to radio (e.g., FM radio), satellite andWiFi networks.

While this document contains many specific implementation details, theimplementation details should not be construed as limitations on thescope of what may be claimed but rather as a description of featuresthat may be specific to particular embodiments. Certain features thatare described in this specification in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to a subcombination or variation of a sub combination.

While logic flows or operations are depicted in the drawings in aparticular order, this should not be understood as requiring that suchoperations be performed in the particular order shown or in sequentialorder, or that all illustrated operations be performed, to achievedesirable results. In certain circumstances, multitasking and parallelprocessing may be advantageous. Moreover, the separation of varioussoftware components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described software components cangenerally be integrated together in a single software program ormultiple software programs.

Some aspects of the subject matter of this specification may includegathering and use of data available from various sources. The presentdisclosure contemplates that in some instances, this gathered data mayidentify a particular location or an address based on device usage. Suchpersonal information data can include location-based data, addresses,subscriber account identifiers, or other identifying information. Thepresent disclosure further contemplates that the entities responsiblefor the collection, analysis, disclosure, transfer, storage, or otheruse of such personal information data will comply with well-establishedprivacy policies and/or privacy practices. In particular, such entitiesshould implement and consistently use privacy policies and practicesthat are generally recognized as meeting or exceeding industry orgovernmental requirements for maintaining personal information dataprivate and secure.

What is claimed is:
 1. A method comprising: receiving, by the one ormore processors of an over-the-air (OTA) mobility services platform, aplurality of user inputs specifying a mobility client model and aplurality of software models that are associated with the mobilityclient model, the mobility client model defining a plurality of hardwarecomponents installed on a mobility client that execute software definedby the software models, the plurality of software models also defininginterdependencies between one or more versions of the plurality ofsoftware models; generating, by the one or more processors, update filesfor software updates stored in a data repository and downloadinformation files including instructions for downloading and installingthe update files, the instructions defining an installation orderhierarchy that preserves the interdependencies between the one or moreversions of the plurality of software models; generating, by the one ormore processors, a distribution task, the distribution task including astart time, an end time and a mobility client group; generating, by theone or more processors, installation tasks for mobility clients in themobility client group; in accordance with the installation tasks:transferring, by the one or more processors, the download informationfiles to the mobility clients in the mobility client group; and inaccordance with the download information files, transferring the updatefiles to the mobility clients in the mobility client group.
 2. Themethod of claim 1, further comprising: monitoring, by the one or moreprocessors, the installation tasks to detect an issue with theinstallation tasks; in accordance with detecting an issue and responsiveto user inputs: assigning, by the one or more processors, the issue to amember of a user class of the mobility services platform based on anissue type; and changing, by the one or processors, a status of theinstallation tasks after the issue is resolved.
 3. The method of claim1, further comprising: determining, by the one or more processors, themobility client group based on location data provided by the mobilityclients in the mobility client group.
 4. The method of claim 3, whereinthe mobility client group is determined based on the location data and ageofence.
 5. The method of claim 1, wherein the OTA mobility servicesplatform is a distributed computing platform that includes a data streamprocessing pipeline architecture for processing data feeds from mobilityclients using a scalable publication/subscription message queue as adistributed transaction log.
 6. The method of claim 1, wherein the OTAmobility services platform comprises multiple instances of software thatrun concurrently and communicate with each other over a message bus. 7.The method of claim 1, further comprising: creating, by the OTA mobilityservices platform, virtual representations of the mobility clients thatinclude unique identifiers for the mobility clients and data structureswith one or more fields that provide an interface operable for sendingand receiving data or services from one or more data or serviceproviders that are external to the OTA mobility services platform; andstoring the virtual representations in one or more databases accessibleby the OTA mobility services platform.
 8. A method comprising:receiving, by one or more processors of a mobility client of anover-the-air (OTA) mobility services platform, a download informationfile including instructions for downloading and installing update filesfrom a data repository, the instructions defining an installation orderhierarchy that preserves interdependencies between one or more versionsof a plurality of software models defining software installed on themobility client; in accordance with the instructions, downloading theupdate files to the mobility client; and in accordance with theinstallation order hierarchy, initiating, by the one or more processors,installation of the update files on the mobility client.
 9. The methodof claim 8, wherein the installation order hierarchy is determined by atopological sort.
 10. The method of claim 8, wherein the downloadinformation file includes links to the update files stored in anetwork-based data repository.
 11. An over-the-air (OTA) mobilityservices platform, comprising: one or more processors; memory; and oneor more programs stored in the memory, that when executed by the one ormore processors, cause the one or more processors to perform operationscomprising: receiving a plurality of user inputs specifying a mobilityclient model and a plurality of software models that are associated withthe mobility client model, the mobility client model defining aplurality of hardware components installed on a mobility client thatexecute software defined by the software models, the plurality ofsoftware models also defining interdependencies between one or moreversions of the plurality of software models; generating update filesfor software updates stored in a data repository and downloadinformation files including instructions for downloading and installingthe update files, the instructions defining an installation orderhierarchy that preserves the interdependencies between the one or moreversions of the plurality of software models; generating a distributiontask, the distribution task including a start time, an end time and amobility client group; generating installation tasks for mobilityclients in the mobility client group; in accordance with theinstallation tasks: transferring, by the one or more processors, thedownload information files to the mobility clients in the mobilityclient group; and in accordance with the download information files,transferring the update files to the mobility clients in the mobilityclient group.
 12. The OTA mobility services platform of claim 11,wherein the operations further comprise: monitoring, the installationtasks to detect an issue with the installation tasks; in accordance withdetecting an issue and responsive to user inputs: assigning the issue toa member of a user class of the mobility services platform based on anissue type; and changing a status of the installation tasks after theissue is resolved.
 13. The OTA mobility services platform of claim 11,wherein the operations further comprise: determining the mobility clientgroup based on location data provided by the mobility clients in themobility client group.
 14. The OTA mobility services platform of claim13, wherein the mobility client group is determined based on thelocation data and a geofence.
 15. The OTA mobility services platform ofclaim 11, wherein the OTA mobility services platform is a distributedcomputing platform that includes a data stream processing pipelinearchitecture for processing data feeds from mobility clients using ascalable publication/subscription message queue as a distributedtransaction log.
 16. The OTA mobility services platform of claim 11,wherein the OTA mobility services platform comprises multiple instancesof software that run concurrently and communicate with each other over amessage bus.
 17. The OTA mobility services platform of claim 11, furthercomprising: creating, by the OTA mobility services platform, virtualrepresentations of the mobility clients that include unique identifiersfor the mobility clients and data structures with one or more fieldsthat provide an interface operable for sending and receiving data orservices from one or more data or service providers that are external tothe OTA mobility services platform; and storing the virtualrepresentations in one or more databases accessible by the OTA mobilityservices platform.
 18. A mobility client of an over-the-air (OTA)mobility services platform, comprising: one or more processors; memory;and one or more programs stored in the memory, that when executed by theone or more processors, cause the one or more processors to performoperations comprising: receiving a download information file includinginstructions for downloading and installing update files from a datarepository, the instructions defining an installation order hierarchythat preserves interdependencies between one or more versions of aplurality of software models defining software installed on the mobilityclient; in accordance with the instructions, downloading the updatefiles to the mobility client; and in accordance with the installationorder hierarchy, initiating, by the one or more processors, installationof the update files on the mobility client.
 19. The mobility client ofclaim 18, wherein the installation order hierarchy is determined by atopological sort.
 20. The mobility client of claim 18, wherein thedownload information file includes links to the update files stored in anetwork-based data repository.